Method and system for creating an audience list based on user behavior data

ABSTRACT

Disclosed is a computer implemented method of creating an audience list based on user behavior data. The method may include receiving, by a processor, filtering criteria for identifying the audience list. Further, the method may include filtering, by the processor, set of users based on the filtering criteria, wherein each user in the set of users is associated with user behavior data. Additionally, the method may include identifying, by the processor, a plurality of users from the set of users based on the filtering, wherein the plurality of users constitutes the audience list.

RELATED APPLICATIONS

Under provisions of 35 U.S.C. §119(e), the Applicant claims the benefit of U.S. provisional application No. 62/173,071, filed Jun. 9, 2015, which is incorporated herein by reference.

The following related U.S. patent applications, filed on even date herewith in the name of Clickagy, LLC, assigned to the assignee of the present application, are hereby incorporated by reference:

-   -   Attorney Docket No. E279P.001US01, entitled “METHOD, SYSTEM AND         COMPUTER READABLE MEDIUM FOR CREATING A PROFILE OF A USER BASED         ON USER BEHAVIOR;”     -   Attorney Docket No. E279P.001US02, entitled “METHOD AND SYSTEM         FOR PROVIDING BUSINESS INTELLIGENCE BASED ON USER BEHAVIORA;”         and     -   Attorney Docket No. E279P.001US04, entitled “METHOD AND SYSTEM         FOR INFLUENCING AUCTION BASED ADVERTISING OPPORTUNITIES BASED ON         USER CHARACTERISTICS.”

It is intended that each of the referenced applications may be applicable to the concepts and embodiments disclosed herein, even if such concepts and embodiments are disclosed in the referenced applications with different limitations and configurations and described using different examples and terminology.

FIELD OF DISCLOSURE

The present disclosure generally relates to field of communicating information to targeted users. More specifically, the present disclosure relates to a method, system and computer readable medium for creating an audience list based on user behavior data.

BACKGROUND

Individuals and companies often use data derived from the Internet to optimize business strategies. For example, data derived from the Internet may be used to study demographics, psychographics, market behavior, competitor affinity, targeted marketing, and expanding markets. For example, companies often use market data to best market their products and services. Moreover, companies often use targeted marketing to specific individuals to try to improve marketing effectiveness.

When consumers visit a website, the pages they visit, the amount of time they view each page, the links they click on, the searches they make and the things that they interact with, allow sites to collect that data, and other factors, create a ‘profile’ that links to that visitor's web browser. As a result, companies can use this data to create defined audience segments based upon visitors that have similar profiles. When visitors return to a specific site or a network of sites using the same web browser, those profiles can be used to allow advertisers to position their online advertisements in front of those visitors who exhibit a greater level of interest and intent for the products and services being offered. On the theory that properly targeted advertisements may fetch more consumer interest, the publisher (or seller) can charge a premium for these advertisements over random advertising or advertisements based on the context of a site.

Behavioral marketing can be used on its own or in conjunction with other forms of targeting based on factors like geography, demographics or contextual web page content. While there is an abundance of data from global Internet use, much of the data is unavailable due to privacy laws. The information that is available is often too general to be useful and does not provide adequate resolution.

BRIEF OVERVIEW

A platform for creating an audience list based on user behavior data may be provided. This brief overview is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This brief overview is not intended to identify key features or essential features of the claimed subject matter. Nor is this brief overview intended to be used to limit the claimed subject matter's scope.

According to some embodiments, a computer implemented method of creating an audience list based on user behavior data may be provided. The method may include receiving a filtering criteria for identifying the audience list. The method may further include filtering a set of users based on the filtering criteria such that each user in the set of users may be associated with user behavior data. Further, the method may include identifying multiple users from the set of users based on the filtering a set of users. The identified multiple users constitutes the audience list.

Further, in some embodiments, the filtering criteria may include one or more values corresponding to one or more parameters, which may include a keyword, an affinity of the keyword, a domain name, a webpage identifier and time corresponding to capturing of the user behavior data. The user behavior data may be characterized by the one or more parameters. Further, the filtering criteria may include a logical expression based on multiple filtering criterions. Each filtering criterion may include one or more values corresponding to the one or more parameters. Additionally, logical expression may be based on logical operators including OR operator, AND operator, NOT operator, XOR operator, NOR operator and XNOR operator. In some embodiments, an advertisement may be presented to the multiple users in the audience list. The advertisement may be a contextual advertisement.

In a further embodiment, the method may include detecting an update to the user behavior data resulting in an updated user behavior data; filtering the updated user behavior data based on the filtering criteria; and identifying an updated plurality of users constituting an updated audience list. This helps in keeping the audience list up-to-date in response to new data.

In some embodiments, the method may include receiving a notification of an opportunity to present an advertisement to a user. Then the method may include determining the presence of the user in the audience list and identifying an advertisement associated with the audience list. Thereafter, the method may include transmitting a bid for presenting the advertisement. Additionally, the method may include receiving a context corresponding to the opportunity. Finally, the method may include receiving a notification of a bid win and presenting the advertisement to the one or more users.

In another embodiment, the method may include receiving multiple user identifiers corresponding to the set of users. The method may include aggregating the multiple user identifiers corresponding to the set of users from multiple data sources. The user behavior data may be anonymous. Further, user behavior data corresponding to a user may include multiple keywords representing an interest of the user and multiple affinity values corresponding to the multiple keywords. Each of the multiple keywords and the multiple affinity values may be determined based on an analysis of content present on a webpage visited by the user. The content may include textual content and non-textual content, such that the analysis may include conversion of non-textual content into textual content. Further, the analysis may include natural language processing of the content.

Further, in another embodiment, the audience list may be associated with an audience identifier. The filtering may include executing a query on a database comprising the user behavior data based on the filtering criteria, such that a result of executing the query may include user behavior data corresponding to the multiple users. The parameters may include a demographic variable and a psychographic variable. The parameter may include a contextual variable. The filtering criteria may also include an ordered list of a value.

In another embodiment, disclosed may be an audience system for a computer operating system installed on a computer including processors and storage mediums containing instructions configured to cause the processors to perform certain operations. These operations may include receiving a filtering criteria for identifying the audience list, filtering a set of users based on the filtering criteria and identifying a plurality of users from the set of users based on the filtering. The multiple users constitute the audience list.

In another embodiment, disclosed may be an audience system including a processor; and a memory coupled to the processor. The memory may include a receiving module configured to receive a filtering criteria for identifying the audience list. The memory further may include a filtering module configured to filter a set of users based on the filtering criteria. Each user in the set of users may be associated with user behavior data. The memory further may include an identification module configured to identify a plurality of users from the set of users based on the filtering. The multiple users constitute the audience list.

Both the foregoing brief overview and the following detailed description provide examples and are explanatory only. Accordingly, the foregoing brief overview and the following detailed description should not be considered to be restrictive. Further, features or variations may be provided in addition to those set forth herein. For example, embodiments may be directed to various feature combinations and sub-combinations described in the detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present disclosure. The drawings contain representations of various trademarks and copyrights owned by the Applicants. In addition, the drawings may contain other marks owned by third parties and are being used for illustrative purposes only. All rights to various trademarks and copyrights represented herein, except those belonging to their respective owners, are vested in and the property of the Applicants. The Applicants retain and reserve all rights in their trademarks and copyrights included herein, and grant permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.

Furthermore, the drawings may contain text or captions that may explain certain embodiments of the present disclosure. This text is included for illustrative, non-limiting, explanatory purposes of certain embodiments detailed in the present disclosure. In the drawings:

FIG. 1 illustrates a block diagram of an operating environment consistent with the present disclosure;

FIG. 2 is a flow chart of a method for providing an audience list based on user behavior data;

FIG. 3A is a flow chart of a method of creating an audience list;

FIG. 3B is a flow chart of a method of updating the audience list;

FIG. 3C is a flow chart of a presenting an advertisement based on the audience list;

FIG. 4 is a block diagram of an audience system;

FIG. 5 is a user interface for providing a filtering criteria according to an exemplary embodiment;

FIG. 6 is a user interface for presenting results of filtering data according to an exemplary embodiment;

FIG. 7 is a user interface for refine filtering parameters to provide a complex filtering criteria using logic functions (e.g., AND, OR, and NOT) according to an exemplary embodiment;

FIG. 8 illustrates an example user interface of how logic functions may be used;

FIG. 9 is a user interface for presenting results of filtering data based on complex filtering criteria according to an exemplary embodiment;

FIG. 10 illustrates a user interface for analyzing a domain according to an exemplary embodiment;

FIG. 11A illustrates an example of providing a filtering criteria using logic functions according to an exemplary embodiment;

FIG. 11B illustrates an example of contextual targeting according to an exemplary embodiment of FIG. 11A;

FIG. 12 illustrates an online user behavior of a user based on which a user profile may be created in accordance with some embodiments;

FIG. 13 illustrates an exemplary comprehensive user browsing data based on which a user profile may be created in accordance with some embodiments;

FIG. 14 illustrates Natural Language Processing performed on data extracted from webpages visited by a user based on which a user profile may be created in accordance with some embodiments; and

FIG. 15 is a block diagram of a system including a computing device for performing the methods of FIGS. 2 and 3A-C.

DETAILED DESCRIPTION

As a preliminary matter, it may readily be understood by one having ordinary skill in the relevant art that the present disclosure has broad utility and application. As should be understood, any embodiment may incorporate only one or a plurality of the above-disclosed aspects of the disclosure and may further incorporate only one or a plurality of the above-disclosed features. Furthermore, any embodiment discussed and identified as being “preferred” is considered to be part of a best mode contemplated for carrying out the embodiments of the present disclosure. Other embodiments also may be discussed for additional illustrative purposes in providing a full and enabling disclosure. As should be understood, any embodiment may incorporate only one or a plurality of the above-disclosed aspects of the display and may further incorporate only one or a plurality of the above-disclosed features. Moreover, many embodiments, such as adaptations, variations, modifications, and equivalent arrangements, may be implicitly disclosed by the embodiments described herein and fall within the scope of the present disclosure.

Accordingly, while embodiments are described herein in detail in relation to one or more embodiments, it is to be understood that this disclosure is illustrative and exemplary of the present disclosure, and are made merely for the purposes of providing a full and enabling disclosure. The detailed disclosure herein of one or more embodiments is not intended, nor is to be construed, to limit the scope of patent protection afforded in any claim of a patent issuing here from, which scope is to be defined by the claims and the equivalents thereof. It is not intended that the scope of patent protection be defined by reading into any claim a limitation found herein that does not explicitly appear in the claim itself.

Thus, for example, any sequence(s) and/or temporal order of steps of various processes or methods that are described herein are illustrative and not restrictive. Accordingly, it should be understood that, although steps of various processes or methods may be shown and described as being in a sequence or temporal order, the steps of any such processes or methods are not limited to being carried out in any particular sequence or order, absent an indication otherwise. Indeed, the steps in such processes or methods generally may be carried out in various different sequences and orders while still falling within the scope of the present invention. Accordingly, it is intended that the scope of patent protection is to be defined by the issued claim(s) rather than the description set forth herein.

Additionally, it is important to note that each term used herein refers to that which an ordinary artisan would understand such term to mean based on the contextual use of such term herein. To the extent that the meaning of a term used herein—as understood by the ordinary artisan based on the contextual use of such term—differs in any way from any particular dictionary definition of such term, it is intended that the meaning of the term as understood by the ordinary artisan should prevail.

Regarding applicability of 35 U.S.C. §112, ¶6, no claim element is intended to be read in accordance with this statutory provision unless the explicit phrase “means for” or “step for” is actually used in such claim element, whereupon this statutory provision is intended to apply in the interpretation of such claim element.

Furthermore, it is important to note that, as used herein, “a” and “an” each generally denotes “at least one,” but does not exclude a plurality unless the contextual use dictates otherwise. When used herein to join a list of items, “or” denotes “at least one of the items,” but does not exclude a plurality of items of the list. Finally, when used herein to join a list of items, “and” denotes “all of the items of the list.”

The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar elements. While many embodiments of the disclosure may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description does not limit the disclosure. Instead, the proper scope of the disclosure is defined by the appended claims. The present disclosure contains headers. It should be understood that these headers are used as references and are not to be construed as limiting upon the subjected matter disclosed under the header.

The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in, the context of data mining for marketing purposes, embodiments of the present disclosure are not limited to use only in this context. For example, the platform may be used to study demographics, psychographics, market behavior, competitor affinity, and expanding markets.

The disclosure relates to online behavioral advertising. An embodiment discloses an audience system to build an audience list. The disclosed audience system may obtain data corresponding to multiple keywords and anonymous user ID's (computer cookies, for example) and enables a user to create their own filters to generate an “audience list”. The audience list may be used for creating awareness such as, but not limited to, serving online advertisements. The user may be an advertiser or a representative, such as a webserver or an ad server administrator. The audience list may be compiled from the computer cookie data. Further, the user may take their computer cookie data and provide it to the audience system, which is known as building a first party audience list. Further, the audience system (may also be referred to as ‘audience lab’) enables the user to build their own first party (may also be referred to as ‘first party’) audience list (if user's doesn't already have one) by providing it with a line of code to place into the user's website. This may enable the tracking of all traffic on the user's website, thereby building a first party audience list.

In further embodiments, the user may build a third party (may also be referred to as ‘third party’) audience list. This may allow the user to create their list from the audience system's database of computer cookies, which is aggregated from many different sources. Further, new audience lists are created by the user inputting multiple parameters by which the first party or third party audience list should be filtered. These lists may then serve as the basis of, for example, but not limited to, creating awareness or online advertisements. Further, in an exemplary embodiment, the present audience system looks at the pure factual/historical data of the user (e.g., traffic history/websites/keywords). The audience system enables users to ensure that their advertisements are served to the right audience, rather than to a generic audience, thereby creating a more cost-effective and higher-conversion marketing system.

I. Platform Overview

Consistent with embodiments of the present disclosure, an audience creation platform may be provided. This overview is provided to introduce a selection of concepts in a simplified form that are further described below. This overview is not intended to identify key features or essential features of the claimed subject matter. Nor is this overview intended to be used to limit the claimed subject matter's scope.

The audience creation platform consistent with embodiments of the present disclosure may be used by individuals or companies to determine, with relative accuracy, statistics about individuals using the Internet and groups of such individuals. Such statistics may be used by the audience creation platform to predict, for example, but not limited to, an individual or group of individuals' personal and commercial behavior. As a non-limiting, illustrative example, the audience creation platform may be used by a car company to, for example, determine which individuals are likely to be purchasing a new car, and which brands they are most likely to purchase based on web pages that they visit.

Embodiments of the present disclosure may operate in a plurality of different environments. For example, in a first aspect, the audience creation platform may receive notice that an individual has visited a webpage. Then, the audience creation platform may crawl that page to gather raw data from the page. For example, the audience creation platform may use various algorithms, including, but not limited to, for example, natural language processing (NLP) and digital signal processing (audio/image/video data) to search the web page for key words or phrases. For instance, as illustrated in FIG. 14, analyzing content of the webpage using, for example, NLP may result in identification of a category of content, such as “Entertainment”. Further, NLP may also identify brand affinities of the webpage, such as for example, “Star Wars” that may provide a greater contextual relevance and brand awareness to users. Additionally, NLP may also include event detection involving identification of specific time-sensitive triggers, such as for example, an upcoming “New Movie”. Further, NLP may also identify important topics addressed in the content of the webpage and associate those topics as concept tags with the webpage, such as for example, “Cinema”. Further, NLP may also include entity extraction involving identifying relevant proper nouns like people and/or brands.

Still consistent with embodiments of the present disclosure, the audience creation platform may receive raw data as it tracks individuals throughout, for example, an ad network or collection of ad networks. Tracking may include, for example, but not be limited to, a crawling of each visited webpage so as to create a profile for the page. As may be further detailed below, the profile may be generated by, for example, the aforementioned algorithms used to gather raw data for the page.

In yet further embodiments of the present disclosure, the raw data may be from purchased data acquired by data aggregators. The raw data may include, for example, a plurality of device specific information (e.g., device serial number, IP address, and the like) along with a listing of websites accessed by the device. The audience creation platform may be enabled to identify a plurality of devices associated with a single individual and, subsequently, associated the data aggregated and processed for each device to a single individual profile.

The audience creation platform may then apply the aforementioned algorithms to process the websites accessed by the devices and, in this way, profile the websites as may be detailed below. The profiled website may then be used to characterize an individual who has been detected to access the profiled website. Moreover, and as may be further detailed below, the characterized individual data may then be grouped along with other individuals' data assessed by the audience creation platform in a plurality of ways including, but not limited to, geographic, household, workplace, interests, affinities, gender, age, and the like.

It should be understood that each individual analyzed by the audience creation platform of the present disclosure may be weighted with an ‘affinity’ of relationship to a particular category. For example, for those individuals who have visited websites profiled to be more ‘female’ friendly may be determined, by the audience creation platform, to be most likely a ‘female’ based on, either solely or at least in part, the individuals web-traffic of profiled webpages associated with the individuals tracked device.

As yet a further example, the audience creation platform may identify individuals that visit webpages that include the words “cell phone” and determine that the individuals may be more likely to be shopping for cell phones. Further, by counting the number of times the individuals visit web pages that have predominately iPhones versus web pages that have predominately Android phones, the likelihood that such individuals prefer one phone to the other may be assessed. The audience creation platform may group like users to create useful statistical data. For example, the audience creation platform may create groups of people that are most likely willing to purchase a specific product (e.g., cell phones, or, more specifically, Android smartphones).

Embodiments of the audience creation platform may further be used to enable a platform user (e.g., mobile telecommunications company) to better understand its target market. Accordingly, data that has been acquired, aggregated, and processed by the audience creation platform may be provided to the user. For example, an application program interface (API) may provide statistics about single individuals (e.g., likelihood that an individual prefers Android phones to iPhones), or groups of individuals (e.g., which individuals prefer Android phones to iPhones). Such statistics may be provided in, for example, lists, charts, and graphs. Further, searchable and sortable raw data may be provided. In some embodiments, the data may be provided to licensed users. For example, users that have identified data such as, for example, AT&T, which has a list of known individuals, may use the data to, for example, further market to their known list of individuals or predict churn.

In some embodiments, the processed data may be provided to the user as a plug-in. For example, if an individual logs into a website for the first time (e.g., Home Depot), the website owner may be able to customize the display for the first-time individual. In other embodiments, the audience creation platform may integrate with a customer relationship module (CRM). In this way, the CRM may be automatically updated with processed data for individuals in the CRM.

In some embodiments, a method for creating an audience list is disclosed. The method includes receiving filtering criteria from an advertiser, then filtering the user behavior data or website data based on filtering criteria. Thereafter, multiple users are identified to obtain an audience list. Moreover, an advertisement may be displayed on a webpage via an exchange. The advertisement may be a contextual advertisement. An exemplary online user behavior data based on which a user profile may be created is illustrated in FIG. 12.

Both the foregoing overview and the following detailed description provide examples and are explanatory only. Accordingly, the foregoing overview and the following detailed description should not be considered to be restrictive. Further, features or variations may be provided in addition to those set forth herein. For example, embodiments may be directed to various feature combinations and sub-combinations described in the detailed description.

II. Platform Configuration

FIG. 1 illustrates one possible operating environment through which a platform consistent with embodiments of the present disclosure may be provided. By way of non-limiting example, a platform 100 may be hosted on a centralized server 110, such as, for example, a cloud computing service. A user 105 may access platform 100 through a software application. The software application may be embodied as, for example, but not be limited to, a website, a web application, a desktop application, and a mobile application compatible with a computing device 1500. One possible embodiment of the software application may be provided by Clickagy, LLC.

As may be detailed with reference to FIG. 15 below, the computing device through which the platform may be accessed may include, but not be limited to, for example, a desktop computer, laptop, a tablet, or mobile telecommunications device. Though the present disclosure is written with reference to a mobile telecommunications device, it should be understood that any computing device may be employed to provide the various embodiments disclosed herein.

The user 105 may provide input parameters to the platform 100. For example, input parameters may be certain device IDs. As another example, input parameters may include individuals living in Atlanta, Ga. Input parameters may be passed to server 110. Server 110 may further be connected to various databases, such as, for example, purchased data 120, tracking data 125 and CRM data 130. In some embodiments, the CRM may be associated with the user. For example, user's CRM database may interface with the platform 100.

Information relevant to individuals associated with the input parameters, such as, for example, which websites they visited, may be sent to web crawler 115. Web crawler 115 may search web pages and online documents visited by individuals being tracked and gather data associated with the searched web pages and online documents. For example, web crawler 115 may utilize natural language processing and audio, video and image processing to gather information for websites. Web crawler 115 may further perform algorithms and build profiles based on web pages and online documents being searched, such as, for example, constructing ‘affinities’ for websites (further discussed below). Information and website and online document profiles being tracked may be passed back to server 110. Server 110 may further construct profiles for individuals being tracked and groups of individuals being tracked. The individual and group profiles as well as further data (e.g. personally identifiable information (PPI), non-PPI, de-identified data, website/individual/group affinity and audience list) may be returned to the user 105.

The user 105 may then use the returned data. For example, the user 105 may merge the individual and group profiles with their own data. In some embodiments, the user 105 may license the data to other individuals or companies. In further embodiments, the user 105 may receive data in a visual form, such as, for example, on a dashboard containing tables, graphs, and charts summarizing the data. In some embodiments, received data may be integrated with a user CRM database. Further, in some embodiments, the received data may be utilized by an API. For example, a plug-in may utilize the received data for identifying individuals (and their associated information, affinities and preferences) that visit a user's website for the first time.

III. Platform Operation

FIGS. 2 and 3A-C are flow charts setting forth the general stages involved in methods 200 and 300A to 300C consistent with an embodiment of the disclosure for providing a platform 100 for creating an audience list based on user behavior data. Methods 200 and 300A to 300C may be implemented using a computing device 1500 as described in more detail below with respect to FIG. 15.

Although methods 200 and 300A to 300C have been described to be performed by platform 100, it should be understood that the computing device 1500 may be used to perform the various stages of methods 200 and 300A to 300C. Furthermore, in some embodiments, different operations may be performed by different networked elements in operative communication with the computing device 1500. For example, server 110 may be employed in the performance of some or all of the stages in methods 200 and 300A to 300C. Moreover, server 110 may be configured much like the computing device 1500.

In an alternate embodiment, an audience system 400 shown in FIG. 4 may be used to execute the methods 300A to 300C.

Although the stages illustrated by the flow charts are disclosed in a particular order, it should be understood that the order is disclosed for illustrative purposes only. Stages may be combined, separated, reordered, and various intermediary stages may exist. Accordingly, it should be understood that the various stages illustrated within the flow chart may be, in various embodiments, performed in arrangements that differ from the ones illustrated. Moreover, various stages may be added or removed from the flow charts without altering or deterring from the fundamental scope of the depicted methods and systems disclosed herein. Ways to implement the stages of methods 200 and 300A to 300C may be described in greater detail below.

According to an exemplary embodiment, the present disclosure relates to online behavioral advertising to serve advertisements to visitors based on historical data and characteristics of the web visitor. The historical data and visitor characteristics may be determined based on combinations of keywords extracted from their browsing history (groups based on high keyword affinity). Further, the present disclosure enables advertisers to ensure that their advertisements are being served to the right audience, rather than a generic audience, thereby creating a more cost-effective and higher-conversion marketing system. For instance, an exemplary set of keywords identified for a user based on the user's interaction with various webpages is illustrated in FIG. 13. For example, based on the user's visiting of a webpage related to sports news, the keywords “Football” and “Basketball” may be identified and associated with the user. Accordingly, the plurality of keywords may indicate one or more topics of interest to the user. For instance, the plurality of keywords may be extracted from webpages visited by the user.

FIG. 3A illustrates exemplary flow chart corresponding to the method 300A for creating an audience list based on user behavior data. At step 302, the method includes receiving, by a processor, a filtering criteria for identifying an audience list from the user 105. The user 105 may be an advertiser. The user 105 may use a user interface 500 shown in FIG. 5 to provide the filtering criteria for identifying an audience list. For example, the user 105 may use a user interface element 502 (for example, a button or a text box) to provide a name, such as “Corvette”, to the audience list to be identified. The user 105 may also use the user interface 500 to choose between getting a first party data and getting a third party data, such that, for first party data, a customer uses their own database of web-user records. For third party data, the computing device 1500 provides web-user data for analysis or prospecting. Then, the user 105 may provide the filtering criteria in a user interface element 504, such as a text box. For example, the filtering criteria may be provided in keyword, time, relevancy format, such as, “Keyword: Corvette within 30 days and a relevancy of 50%”. Here, the user 105 is requesting to filter the audience list down to users who have a relevancy score of 50% for the word “Corvette” in the past 30 days.

At step 304, the method includes filtering, by a processor, a set of users based on the filtering criteria (provided in the user interface element 504), wherein each user in the set of users is associated with the expected user behavior data. Filtering includes executing a query on a database with the user behavior data based on the filtering criteria.

Then, at step 306, the method includes identifying, multiple users based on filtering. The identified users constitute the audience list. For example, the computing device 1500 may search through the available user data (based on the filtering criteria) to identify that there are 52,858 web-pages that have been visited by approximately 3.6 million users (cookies). This information may be displayed in a user interface element 602 (for example, a display box) as shown in FIG. 6. Therefore, the user 105 has specified that the advertisement should only be served to those users who are filtered by a predetermined campaign audience list created by an audience system.

Moreover, the user 105 can further refine the audience data with “AND” and “OR” and “NOT” conditions to more particularly refine the audience list. As shown in FIG. 7 (a user interface 700 is shown), the user 105 may use a user interface element 702 to drop an “AND” condition and a user interface element 704 to drop a “NOT” condition. For example, as shown in FIG. 8, the user 105 may use the conditional operators available and type of parameters available 706 to provide a complex filtering criteria 802. For example, the type of parameters 706 includes keywords, domain and page URL. Here, the user 105 has used a first set of positive parameters (corvette keywords): “corvette” and “Chevy corvette”. Further, the user 105 has used a second set of positive parameters (purchase indicators): “crash test rating”, “fuel economy” and “safety ratings”. Yet further, the user has used a third set of negative parameters (Video Game Exclusions): “grand theft auto” and “racing game”. The additional filter parameters are meant to be a non-exhaustive list of parameters that can be used to filer the parameters. Using the three set of parameters and various available conditional operators the user 105 has defined the complex filtering criteria 802. Here the computing device 1500 may search through the available user data (based on the complex filtering criteria) to identify that there are 477 web-pages that have been visited by approximately 912 web users (cookies) as shown in the user interface element 902 (for example, a display box) shown in FIG. 9.

Similarly, the user 105 may also include specific domains (using the type of parameters 706), such that the audience list is filtered down to only those web users who has been to particular domains (or have not been to particular domains).

In addition, the user 105 may analyze one or more domain using a user interface 1000. The user may add a domain (for example, goclientside.com) or keywords related to a domain in a user interface element 1002, such as a text box. The computing device 1500 then analyzes the corresponding domain and provides a set of data 1004.

FIG. 11A illustrates an example of providing a filtering criteria using logic functions. A combination of AND, OR, and NOT logic functions (conditional operators) with keywords provide flexibility to the user 105 to combine multiple keywords to drill down and identify the exact audience or pages of relevance to a query. For example, the combination shown in FIG. 11A, allows the user 105 to target web visitors or web pages related to safety aspects of convertible sports car. Further, video games have been removed from this combination to accurately target 23,000 visitors (potential sport cars buyers) with advertisement campaigns.

FIG. 11B illustrates an example of contextual targeting according to the exemplary embodiment of FIG. 11A. The advertisement may be a contextual advertisement. Further, one or more filtering parameters include a contextual variable. In the example shown in FIG. 11B, identified audience is combined with contextual variables. The disclosed keyword attribution engine and NLP technology enhance the way digital ad targeting can be performed with keyword granularity. This can be used to target display banner advertisements, rich media advertisements, video advertisements, mobile advertisements, and any other form of programmatic advertising. For example, the advertisement is displayed based on the next webpage visited by an identified user (a user in the audience list). If the user visits a webpage “acmecars.com” containing a keyword “leasing”, then the advertisement is displayed. However, if the user visits a webpage “sports.com” which is unrelated to car shopping then the advertisement is not displayed.

Further, the parameters defined by the user 105 may be used to filter the constantly updating list of web users and their relevancy scores. FIG. 3B illustrates another exemplary flow chart corresponding to the method 300B for updating an audience list based on updated user behavior data. At step 308, the method 300B includes detecting, by a processor, an update to the user behavior data resulting in an updated user behavior data. Then at step 310, the method 300B includes filtering, by a processor, the updated user behavior data based on the filtering criteria. The step 310 is similar to the step 304. Next at step 312, the method 300B includes identifying, by a processor, an updated list of users constituting an updated audience list, wherein the identifying is based on the filtering. The step 312 is similar to the step 306.

In an embodiment, the present disclosure provides improved methods and systems for demand side platforms (DSP). DSP's are advertisers or group of advertisers represented by an entity. Typically, the DSP have access to the content for the advertisers (their advertisements), the bids that the advertisers are willing to pay for the content, and the criterion based on which their advertisements are to be served. Specifically, the present disclosure provides improved the systems and methods by employing the concept of an “Audience List.”, such that, not only can the Advertiser controls what keywords or websites are to be considered before serving an advertisement to a web visitor, but it also enables the advertiser to specify the required characteristics of the web visitor before bidding on the advertisement placement via an exchange. Further, characteristics of a web visitor are aggregated by the audience system through search of Non-Personally Identifiable Information (PII) associated with web visitors. The non-PII data is aggregated from multiple data providers.

FIG. 3C illustrates an exemplary flow chart corresponding to the method 300C for presenting an advertisement. At step 314, the method includes receiving, by a processor, a notification of an opportunity to present an advertisement to one or more users. For example, the notification may be sent by an exchange in response to a new visitor on a publisher site.

Then at step 316, the method includes determining, by a processor, presence of the one or more users in the audience list. This helps ascertain if the visitor is relevant and if the currently loaded publisher site relevant to audience list parameters.

Next, at step 318, the method includes identifying, by a processor, an advertisement associated with the audience list. Finally, at step 320, the method includes transmitting, by a processor, a bid for presenting the advertisement. The bid may be transmitted to an exchange. Further, the exchange sends an advertisement corresponding to the highest bidder to the corresponding publisher, which publishes the advertisement. A notification of a bid win may also be received.

In some embodiments, the platform may be configured to create and maintain rich user behavior data. Accordingly, the platform may be configured to execute method 200 of creating user profiles based on user behavior data in accordance with some embodiments, is illustrated in FIG. 2.

Method 200 may begin at starting block 205 and proceed to stage 210 where platform 100 may receive data from an individual's internet use. For example, the platform may receive information about a webpage that the individual visited or a Microsoft Word document or PDF that an individual downloaded. Information may include the URL of the webpage. Further information may be received, including IP address of the individual, search history of the individual, and geolocation of the individual.

From stage 210, where platform 100 receives data from an individual's Internet use, method 200 may advance to stage 220 where platform 100 may further gather information associated with the individual's Internet use. For example, the platform may crawl the webpage that the individual visited. For example, the platform may search for specific key words or phrases. In some embodiments, if the webpage has already been crawled, the webpage may be skipped.

During the crawl, the platform may perform, for example, natural language processing (NLP) to further process the context of the words and phrases in the text. In addition, the platform may utilize image recognition, audio recognition, and/or video recognition to gather data about the individual's Internet use. For example, image, video and audio information may be acquired from a webpage “www.example.com” to provide the individual's Internet use information. For instance, images may be scanned with optical character recognition (OCR). The OCR scanning may generate words or phrases for characterizing the webpage. Further, image recognition software may be used to characterize the webpage. For example, artificial intelligence (AI) software may be used to determine whether an image is showing for example, a dog or a tree. Audio files from the webpage may be scanned, using, for example, voice recognition software, to further provide information to characterize the webpage. Video files from a page may be converted to a series of images from periodic individual frames and scanned in the same manner as an image. In addition, the audio associated with the video files may be scanned to provide data about the webpage. Likewise, text from the webpage may also be extracted and analyzed based on NLP. The combination of text, image, audio and video recognition may provide a human-style “view” of what the webpage provides. The human-style “view” may enable the platform to optimize characterization of the webpage.

Information that is acquired from the crawl may further be associated with how recently such information was associated with the webpage (e.g., newer information may be given a higher relevance than older information). The platform may receive further information, for example, that is purchased from various data aggregators (e.g., aggregators that track specific IDs.) In addition, information may be tracked from an existing individual base. For example, if the individual clicks (“I Agree”) on certain terms and conditions, the platform may place a tracking cookie on the individual's device to further gather information. In some embodiments, stages 210 and 220 may comprise 207, where platform 100 receives general data. The general data may include, for example, data from webpages (e.g., text, image, audio, and video data associated with the webpage) and data from individuals (e.g., which websites the individuals have visited, information from the individuals' social media profiles, and the like).

Once platform 100 further gathers information associated with the individual's Internet use in stage 220, method 200 may continue to stage 230 where platform 100 may analyze the information. In some embodiments, the platform may perform natural language processing (NLP) as well as image, audio and video recognition to analyze the information. For example, the platform may use specific keywords and phrases, as well as keywords associated with image, video and audio files, found on each webpage and attach a plurality of ‘affinities’ to each page. For example, for a news article about iPhones, the platform may return hundreds of ‘keywords’, including “Apple” with 94% affinity, “cell phone” with 81% affinity, and “screen” with 52% affinity. The platform may then interpret the information based on the individual's Internet use to create a profile associated with the affinities.

For example, an individual may visit a number of webpages that have high affinity for keywords like “truck”, “football”, and “Scotch”. Such an individual may be statistically more likely to be a male. As another example, another individual may visit a number of webpages that have high affinity for keywords like “nail polish”, “Midol”, and “Pinterest.” Such an individual may be statistically more likely to be female. Such statistical predictions may be associated with a confidence level. Further, statistical predictions may be made for an abundance of other characteristics, such as, for example, but not limited to, age, marital status, parental status, approximate household income, industry of employment, sport preference, automobile preference, and phone preference.

After platform 100 analyzes the information for each individual in stage 230, method 200 may proceed to stage 240 where platform 100 may group users based on certain characteristics. For example, individuals likely to be of a certain characteristic, such as, for example, gender, age, marital status, parental status, approximate household income, and industry of employment, may be grouped together. Additionally, individuals may be grouped together based on their preferences, such as, for example, sport preference, automobile preference, and phone preference.

IV. Platform Architecture

The audience list based on user behavior data platform 100 may be embodied as, for example, but not be limited to, a website, a web application, a desktop application, and a mobile application compatible with a computing device. The computing device may include, but not be limited to, a desktop computer, laptop, a tablet, or mobile telecommunications device. Moreover, the platform 100 may be hosted on a centralized server, such as, for example, a cloud computing service. Although the methods 200 and 300A to 300C has been described to be performed by a computing device 1500, it should be understood that, in some embodiments, different operations may be performed by different networked elements in operative communication with computing device 1500.

Embodiments of the present disclosure may include a system having memory storage and a processing unit. The processing unit coupled to the memory storage, wherein the processing unit is configured to perform the stages of the methods 200 and 300A to 300C.

FIG. 15 is a block diagram of a system including computing device 1500. Consistent with an embodiment of the disclosure, the aforementioned memory storage and processing unit may be implemented in a computing device, such as computing device 1500 of FIG. 15. Any suitable combination of hardware, software, or firmware may be used to implement the memory storage and processing unit. For example, the memory storage and processing unit may be implemented with computing device 1500 or any of other computing devices 1518, in combination with computing device 1500. The aforementioned system, device, and processors are examples and other systems, devices, and processors may include the aforementioned memory storage and processing unit, consistent with embodiments of the disclosure.

With reference to FIG. 15, a system consistent with an embodiment of the disclosure may include a computing device, such as computing device 1500. In a basic configuration, computing device 1500 may include at least one processing unit 1502 and a system memory 1504. Depending on the configuration and type of computing device, system memory 1504 may comprise, but is not limited to, volatile (e.g. random access memory (RAM)), non-volatile (e.g. read-only memory (ROM)), flash memory, or any combination. System memory 1504 may include operating system 1505, one or more programming modules 1506, and may include a program data 1507. Operating system 1505, for example, may be suitable for controlling computing device 1500's operation. In one embodiment, programming modules 1506 may include affinity calculating modules, such as, for example, webpage affinity calculation application 1520. Furthermore, embodiments of the disclosure may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated in FIG. 15 by those components within a dashed line 1508.

Computing device 1500 may have additional features or functionality. For example, computing device 1500 may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in FIG. 15 by a removable storage 1509 and a non-removable storage 1510. Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. System memory 1504, removable storage 1509, and non-removable storage 1510 are all computer storage media examples (i.e., memory storage.) Computer storage media may include, but is not limited to, RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device 1500. Any such computer storage media may be part of device 1500. Computing device 1500 may also have input device(s) 1512 such as a keyboard, a mouse, a pen, a sound input device, a touch input device, etc. Output device(s) 1514 such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used.

Computing device 1500 may also contain a communication connection 1516 that may allow device 1500 to communicate with other computing devices 1518, such as over a network in a distributed computing environment, for example, an intranet or the Internet. Communication connection 1516 is one example of communication media. Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media. The term computer readable media as used herein may include both storage media and communication media.

As stated above, a number of program modules and data files may be stored in system memory 1504, including operating system 1505. While executing on processing unit 1502, programming modules 1506 (e.g., platform application 1520) may perform processes including, for example, one or more of methods 200 and 300A to 300C's stages as described above. The aforementioned process is an example, and processing unit 1502 may perform other processes. Other programming modules that may be used in accordance with embodiments of the present disclosure may include electronic mail and contacts applications, word processing applications, spreadsheet applications, database applications, slide presentation applications, drawing or computer-aided application programs, etc.

FIG. 4 illustrates an exemplary embodiment of the audience system 400 for creating an audience list based on user behavior data, such that, the audience system comprises a processor 410 coupled to a memory 420, such that, the memory further includes: a receiving module 430 configured to, receive a filtering criteria for identifying the audience list; a filtering module 440 configured to, filter a set of users based on the filtering criteria, wherein each user in the set of users is associated with user behavior data; and an identification module 450 configured to, identify a plurality of users from the set of users based on the filtering, wherein the plurality of users constitutes the audience list.

Generally, consistent with embodiments of the disclosure, program modules may include routines, programs, components, data structures, and other types of structures that may perform particular tasks or that may implement particular abstract data types. Moreover, embodiments of the disclosure may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. Embodiments of the disclosure may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Furthermore, embodiments of the disclosure may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors. Embodiments of the disclosure may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the disclosure may be practiced within a general purpose computer or in any other circuits or systems.

Embodiments of the disclosure, for example, may be implemented as a computer process (method), a computing system, or as an article of manufacture, such as a computer program product or computer readable media. The computer program product may be a computer storage media readable by a computer system and encoding a computer program of instructions for executing a computer process. The computer program product may also be a propagated signal on a carrier readable by a computing system and encoding a computer program of instructions for executing a computer process. Accordingly, the present disclosure may be embodied in hardware and/or in software (including firmware, resident software, micro-code, etc.). In other words, embodiments of the present disclosure may take the form of a computer program product on a computer-usable or computer-readable storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system. A computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

The computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific computer-readable medium examples (a non-exhaustive list), the computer-readable medium may include the following: an electrical connection having one or more wires, a portable computer diskette, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CD-ROM). Note that the computer-usable or computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

Embodiments of the present disclosure, for example, are described above with reference to block diagrams and/or operational illustrations of methods, systems, and computer program products according to embodiments of the disclosure. The functions/acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved.

While certain embodiments of the disclosure have been described, other embodiments may exist. Furthermore, although embodiments of the present disclosure have been described as being associated with data stored in memory and other storage mediums, data can also be stored on or read from other types of computer-readable media, such as secondary storage devices, like hard disks, solid state storage (e.g., USB drive), or a CD-ROM, a carrier wave from the Internet, or other forms of RAM or ROM. Further, the disclosed methods' stages may be modified in any manner, including by reordering stages and/or inserting or deleting stages, without departing from the disclosure.

All rights including copyrights in the code included herein are vested in and the property of the Applicant. The Applicant retains and reserves all rights in the code included herein, and grants permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.

V. Claims

While the specification includes examples, the disclosure's scope is indicated by the following claims. Furthermore, while the specification has been described in language specific to structural features and/or methodological acts, the claims are not limited to the features or acts described above. Rather, the specific features and acts described above are disclosed as example for embodiments of the disclosure.

Insofar as the description above and the accompanying drawing disclose any additional subject matter that is not within the scope of the claims below, the disclosures are not dedicated to the public and the right to file one or more applications to claims such additional disclosures is reserved. 

The following is claimed:
 1. A method of creating an audience list based on user behavior data, wherein the method is computer implemented, the method comprising: a. receiving, by a processor, filtering criteria for identifying the audience list; b. filtering, by the processor, set of users based on the filtering criteria, wherein each user in the set of users is associated with user behavior data; and c. identifying, by the processor, a plurality of users from the set of users based on the filtering, wherein the plurality of users constitutes the audience list.
 2. The method of claim 1, wherein the filtering criteria comprises at least one value corresponding to at least one parameter, wherein the user behavior data is characterized by at least one parameter.
 3. The method of claim 2, wherein the at least parameter comprises a keyword, an affinity of the keyword, a domain name, a webpage identifier and time corresponding to capturing of the user behavior data.
 4. The method of claim 2, wherein the filtering criteria comprises logical expression based on a plurality of filtering criteria, wherein each filtering criteria comprises at least one value corresponding to at least one parameter.
 5. The method of claim 4, wherein the logical expression is based on at least one logical operator, the at least one logical operator comprises one of OR operator, AND operator, NOT operator, XOR operator, NOR operator and XNOR operator.
 6. The method of claim 1 further comprising presenting an advertisement to the plurality of users comprised the audience list.
 7. The method of claim 1, wherein the advertisement is a contextual advertisement.
 8. The method of claim 1 further comprising: a. detecting an update to the user behavior data resulting in an updated user behavior data; and b. filtering the updated user behavior data based on the filtering criteria; and c. identifying an updated plurality of users constituting an updated audience list, wherein the identifying is based on the filtering.
 9. The method of claim 1 further comprising: a. receiving a notification of an opportunity to present an advertisement to at least one user; b. determining presence of the at least one user in the audience list; c. identifying an advertisement associated with the audience list; and d. transmitting a bid for presenting the advertisement.
 10. The method of claim 9 further comprising receiving a context corresponding to the opportunity, wherein identifying the advertisement is based further on the context.
 11. The method of claim 9 further comprising: a. receiving a notification of a bid win; and b. presenting the advertisement to the at least one user.
 12. The method of claim 1 further comprising receiving a plurality of user identifiers corresponding to the set of users.
 13. The method of claim 1 further comprising aggregating a plurality of user identifiers corresponding to the set of users from a plurality of data sources.
 14. The method of claim 1, wherein the user behavior data is anonymous.
 15. The method of claim 1, wherein user behavior data corresponding to a user comprises a plurality of keywords representing at least one interest of the user and a plurality of affinity values corresponding to the plurality of keywords, wherein each of the plurality of keywords and the plurality of affinity values is determined based on an analysis of content present on a webpage visited by the user, wherein the content comprises textual content and non-textual content, wherein the analysis comprises conversion of non-textual content into textual content, wherein the analysis further comprises natural language processing of the content.
 16. The method of claim 1, wherein the audience list is associated with an audience identifier.
 17. The method of claim 1, wherein the filtering comprises executing a query on a database comprising the user behavior data based on the filtering criteria, wherein a result of executing the query comprises user behavior data corresponding to the plurality of users.
 18. The method of claim 2, wherein the at least one parameter comprises at least one of a demographic variable and a psychographic variable.
 19. An audience system for a computer operating system installed on a computer comprising: one or more processors; one or more non-transitory computer-readable storage mediums containing instructions configured to cause the one or more processors to perform operations including: receiving a filtering criteria for identifying the audience list; filtering a set of users based on the filtering criteria, wherein each user in the set of users is associated with user behavior data; and identifying a plurality of users from the set of users based on the filtering, wherein the plurality of users constitutes the audience list.
 20. An audience system comprising: a processor; and a memory coupled to the processor, the memory comprising: a receiving module configured to, receive a filtering criteria for identifying the audience list; a filtering module configured to, filter a set of users based on the filtering criteria, wherein each user in the set of users is associated with user behavior data; and an identification module configured to, identify a plurality of users from the set of users based on the filtering, wherein the plurality of users constitutes the audience list. 